Overview

Dataset statistics

Number of variables10
Number of observations82620
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 MiB
Average record size in memory88.0 B

Variable types

Numeric9
Categorical1

Alerts

signal_1 is highly correlated with signal_10High correlation
signal_10 is highly correlated with signal_1 and 1 other fieldsHigh correlation
signal_20 is highly correlated with signal_10 and 2 other fieldsHigh correlation
signal_30 is highly correlated with signal_20 and 2 other fieldsHigh correlation
signal_40 is highly correlated with signal_20 and 2 other fieldsHigh correlation
signal_50 is highly correlated with signal_30 and 1 other fieldsHigh correlation
signal_1 is highly correlated with signal_10 and 3 other fieldsHigh correlation
signal_10 is highly correlated with signal_1 and 3 other fieldsHigh correlation
signal_20 is highly correlated with signal_1 and 3 other fieldsHigh correlation
signal_30 is highly correlated with signal_1 and 3 other fieldsHigh correlation
signal_40 is highly correlated with signal_1 and 4 other fieldsHigh correlation
signal_50 is highly correlated with signal_40High correlation
id is highly correlated with classHigh correlation
signal_1 is highly correlated with signal_10 and 3 other fieldsHigh correlation
signal_10 is highly correlated with signal_1 and 3 other fieldsHigh correlation
signal_20 is highly correlated with signal_1 and 3 other fieldsHigh correlation
signal_30 is highly correlated with signal_1 and 3 other fieldsHigh correlation
signal_40 is highly correlated with signal_1 and 4 other fieldsHigh correlation
signal_50 is highly correlated with signal_40 and 1 other fieldsHigh correlation
signal_60 is highly correlated with signal_50High correlation
class is highly correlated with idHigh correlation
signal_1 is highly skewed (γ1 = 20.85966228) Skewed
signal_10 is highly skewed (γ1 = 20.01932846) Skewed
signal_20 is highly skewed (γ1 = 22.0093275) Skewed
signal_30 is highly skewed (γ1 = 20.54646738) Skewed
signal_40 is highly skewed (γ1 = 23.83916772) Skewed

Reproduction

Analysis started2022-04-19 10:41:07.594885
Analysis finished2022-04-19 10:41:19.413868
Duration11.82 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct204
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.5
Minimum0
Maximum203
Zeros405
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2022-04-19T12:41:19.471882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q150.75
median101.5
Q3152.25
95-th percentile193
Maximum203
Range203
Interquartile range (IQR)101.5

Descriptive statistics

Standard deviation58.8893763
Coefficient of variation (CV)0.5801908995
Kurtosis-1.200057675
Mean101.5
Median Absolute Deviation (MAD)51
Skewness0
Sum8385930
Variance3467.958641
MonotonicityIncreasing
2022-04-19T12:41:19.720939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0405
 
0.5%
140405
 
0.5%
130405
 
0.5%
131405
 
0.5%
132405
 
0.5%
133405
 
0.5%
134405
 
0.5%
135405
 
0.5%
136405
 
0.5%
137405
 
0.5%
Other values (194)78570
95.1%
ValueCountFrequency (%)
0405
0.5%
1405
0.5%
2405
0.5%
3405
0.5%
4405
0.5%
5405
0.5%
6405
0.5%
7405
0.5%
8405
0.5%
9405
0.5%
ValueCountFrequency (%)
203405
0.5%
202405
0.5%
201405
0.5%
200405
0.5%
199405
0.5%
198405
0.5%
197405
0.5%
196405
0.5%
195405
0.5%
194405
0.5%

TimeStamp
Real number (ℝ≥0)

Distinct405
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202
Minimum0
Maximum404
Zeros204
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2022-04-19T12:41:19.814959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q1101
median202
Q3303
95-th percentile384
Maximum404
Range404
Interquartile range (IQR)202

Descriptive statistics

Standard deviation116.9137807
Coefficient of variation (CV)0.5787810924
Kurtosis-1.200014633
Mean202
Median Absolute Deviation (MAD)101
Skewness0
Sum16689240
Variance13668.83211
MonotonicityNot monotonic
2022-04-19T12:41:19.902979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0204
 
0.2%
267204
 
0.2%
277204
 
0.2%
276204
 
0.2%
275204
 
0.2%
274204
 
0.2%
273204
 
0.2%
272204
 
0.2%
271204
 
0.2%
270204
 
0.2%
Other values (395)80580
97.5%
ValueCountFrequency (%)
0204
0.2%
1204
0.2%
2204
0.2%
3204
0.2%
4204
0.2%
5204
0.2%
6204
0.2%
7204
0.2%
8204
0.2%
9204
0.2%
ValueCountFrequency (%)
404204
0.2%
403204
0.2%
402204
0.2%
401204
0.2%
400204
0.2%
399204
0.2%
398204
0.2%
397204
0.2%
396204
0.2%
395204
0.2%

signal_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct16116
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005684799455
Minimum6 × 10-6
Maximum0.88887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2022-04-19T12:41:19.997001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6 × 10-6
5-th percentile0.00052395
Q10.001469
median0.00291
Q30.005639
95-th percentile0.014748
Maximum0.88887
Range0.888864
Interquartile range (IQR)0.00417

Descriptive statistics

Standard deviation0.0179523563
Coefficient of variation (CV)3.157957716
Kurtosis623.0392042
Mean0.005684799455
Median Absolute Deviation (MAD)0.001752
Skewness20.85966228
Sum469.678131
Variance0.0003222870968
MonotonicityNot monotonic
2022-04-19T12:41:20.081019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0013533
 
< 0.1%
0.00076232
 
< 0.1%
0.00111331
 
< 0.1%
0.00081631
 
< 0.1%
0.00069230
 
< 0.1%
0.00178429
 
< 0.1%
0.00137229
 
< 0.1%
0.00108429
 
< 0.1%
0.0014929
 
< 0.1%
0.00159429
 
< 0.1%
Other values (16106)82318
99.6%
ValueCountFrequency (%)
6 × 10-62
< 0.1%
1 × 10-51
 
< 0.1%
1.1 × 10-51
 
< 0.1%
1.6 × 10-53
< 0.1%
1.7 × 10-51
 
< 0.1%
2 × 10-51
 
< 0.1%
2.5 × 10-52
< 0.1%
2.6 × 10-51
 
< 0.1%
2.7 × 10-51
 
< 0.1%
2.9 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.888871
< 0.1%
0.879451
< 0.1%
0.844651
< 0.1%
0.78281
< 0.1%
0.694841
< 0.1%
0.676861
< 0.1%
0.651311
< 0.1%
0.603121
< 0.1%
0.593591
< 0.1%
0.582931
< 0.1%

signal_10
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct19676
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.008572432365
Minimum2.4 × 10-5
Maximum1.7685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2022-04-19T12:41:20.170040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.4 × 10-5
5-th percentile0.000717
Q10.001906
median0.003655
Q30.00724625
95-th percentile0.02203705
Maximum1.7685
Range1.768476
Interquartile range (IQR)0.00534025

Descriptive statistics

Standard deviation0.03333209266
Coefficient of variation (CV)3.888288789
Kurtosis571.6591607
Mean0.008572432365
Median Absolute Deviation (MAD)0.002158
Skewness20.01932846
Sum708.254362
Variance0.001111028401
MonotonicityNot monotonic
2022-04-19T12:41:20.257060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0020530
 
< 0.1%
0.00106729
 
< 0.1%
0.00064428
 
< 0.1%
0.00196826
 
< 0.1%
0.00132426
 
< 0.1%
0.00112926
 
< 0.1%
0.00254326
 
< 0.1%
0.00131826
 
< 0.1%
0.00196925
 
< 0.1%
0.00182225
 
< 0.1%
Other values (19666)82353
99.7%
ValueCountFrequency (%)
2.4 × 10-51
 
< 0.1%
2.6 × 10-51
 
< 0.1%
3 × 10-51
 
< 0.1%
3.3 × 10-52
< 0.1%
3.5 × 10-52
< 0.1%
3.7 × 10-51
 
< 0.1%
4.6 × 10-54
< 0.1%
5 × 10-51
 
< 0.1%
5.1 × 10-51
 
< 0.1%
5.2 × 10-51
 
< 0.1%
ValueCountFrequency (%)
1.76851
< 0.1%
1.59811
< 0.1%
1.50021
< 0.1%
1.37481
< 0.1%
1.34651
< 0.1%
1.09731
< 0.1%
1.08551
< 0.1%
1.07871
< 0.1%
0.997241
< 0.1%
0.980851
< 0.1%

signal_20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct17782
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.009265837328
Minimum3 × 10-6
Maximum2.6202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2022-04-19T12:41:20.348591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3 × 10-6
5-th percentile0.000611
Q10.001624
median0.00305
Q30.005877
95-th percentile0.0204574
Maximum2.6202
Range2.620197
Interquartile range (IQR)0.004253

Descriptive statistics

Standard deviation0.0507875661
Coefficient of variation (CV)5.481163149
Kurtosis659.0755173
Mean0.009265837328
Median Absolute Deviation (MAD)0.001752
Skewness22.0093275
Sum765.54348
Variance0.002579376871
MonotonicityNot monotonic
2022-04-19T12:41:20.442612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00126930
 
< 0.1%
0.00205329
 
< 0.1%
0.00122129
 
< 0.1%
0.00102429
 
< 0.1%
0.00120428
 
< 0.1%
0.00112328
 
< 0.1%
0.00138828
 
< 0.1%
0.00137528
 
< 0.1%
0.00145228
 
< 0.1%
0.00185327
 
< 0.1%
Other values (17772)82336
99.7%
ValueCountFrequency (%)
3 × 10-61
< 0.1%
6 × 10-62
< 0.1%
1 × 10-51
< 0.1%
1.6 × 10-51
< 0.1%
1.9 × 10-51
< 0.1%
2.5 × 10-51
< 0.1%
2.6 × 10-51
< 0.1%
2.9 × 10-51
< 0.1%
3.1 × 10-51
< 0.1%
3.4 × 10-51
< 0.1%
ValueCountFrequency (%)
2.62021
< 0.1%
2.53951
< 0.1%
2.14511
< 0.1%
2.00831
< 0.1%
1.94691
< 0.1%
1.9381
< 0.1%
1.82651
< 0.1%
1.80571
< 0.1%
1.77081
< 0.1%
1.67571
< 0.1%

signal_30
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct18625
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01162100154
Minimum1.1 × 10-5
Maximum3.8421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2022-04-19T12:41:20.533633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 10-5
5-th percentile0.000637
Q10.001629
median0.002971
Q30.005906
95-th percentile0.02442185
Maximum3.8421
Range3.842089
Interquartile range (IQR)0.004277

Descriptive statistics

Standard deviation0.07213075375
Coefficient of variation (CV)6.206930919
Kurtosis581.0113543
Mean0.01162100154
Median Absolute Deviation (MAD)0.001683
Skewness20.54646738
Sum960.127147
Variance0.005202845636
MonotonicityNot monotonic
2022-04-19T12:41:20.623653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00155732
 
< 0.1%
0.00126931
 
< 0.1%
0.00138330
 
< 0.1%
0.00119530
 
< 0.1%
0.00178330
 
< 0.1%
0.00176929
 
< 0.1%
0.00171729
 
< 0.1%
0.00209129
 
< 0.1%
0.00158929
 
< 0.1%
0.0016629
 
< 0.1%
Other values (18615)82322
99.6%
ValueCountFrequency (%)
1.1 × 10-52
< 0.1%
1.4 × 10-51
< 0.1%
1.6 × 10-51
< 0.1%
1.9 × 10-51
< 0.1%
2.4 × 10-52
< 0.1%
2.5 × 10-51
< 0.1%
2.6 × 10-52
< 0.1%
3 × 10-51
< 0.1%
3.2 × 10-52
< 0.1%
3.3 × 10-51
< 0.1%
ValueCountFrequency (%)
3.84211
< 0.1%
2.94311
< 0.1%
2.92771
< 0.1%
2.80051
< 0.1%
2.79911
< 0.1%
2.60691
< 0.1%
2.60261
< 0.1%
2.59931
< 0.1%
2.51421
< 0.1%
2.50711
< 0.1%

signal_40
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct24998
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01817219336
Minimum7 × 10-6
Maximum6.2236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2022-04-19T12:41:20.716675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7 × 10-6
5-th percentile0.000958
Q10.002496
median0.0047425
Q30.010002
95-th percentile0.042734
Maximum6.2236
Range6.223593
Interquartile range (IQR)0.007506

Descriptive statistics

Standard deviation0.1009756214
Coefficient of variation (CV)5.556600651
Kurtosis882.7853151
Mean0.01817219336
Median Absolute Deviation (MAD)0.0028165
Skewness23.83916772
Sum1501.386615
Variance0.01019607612
MonotonicityNot monotonic
2022-04-19T12:41:20.799693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00270225
 
< 0.1%
0.00284325
 
< 0.1%
0.00283123
 
< 0.1%
0.0041623
 
< 0.1%
0.00244722
 
< 0.1%
0.00145822
 
< 0.1%
0.00287522
 
< 0.1%
0.00132521
 
< 0.1%
0.00189721
 
< 0.1%
0.0021421
 
< 0.1%
Other values (24988)82395
99.7%
ValueCountFrequency (%)
7 × 10-61
< 0.1%
1.5 × 10-51
< 0.1%
2.1 × 10-51
< 0.1%
2.3 × 10-51
< 0.1%
2.6 × 10-51
< 0.1%
3.7 × 10-52
< 0.1%
4 × 10-52
< 0.1%
4.9 × 10-51
< 0.1%
5.1 × 10-52
< 0.1%
5.3 × 10-51
< 0.1%
ValueCountFrequency (%)
6.22361
< 0.1%
5.75181
< 0.1%
5.13211
< 0.1%
5.12441
< 0.1%
4.7251
< 0.1%
4.47281
< 0.1%
3.86711
< 0.1%
3.84071
< 0.1%
3.72971
< 0.1%
3.6671
< 0.1%

signal_50
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct46057
Distinct (%)55.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05639586635
Minimum4.1 × 10-5
Maximum7.3434
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2022-04-19T12:41:20.888713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4.1 × 10-5
5-th percentile0.002862
Q10.00804775
median0.016788
Q30.039721
95-th percentile0.1879935
Maximum7.3434
Range7.343359
Interquartile range (IQR)0.03167325

Descriptive statistics

Standard deviation0.1921514678
Coefficient of variation (CV)3.407190637
Kurtosis226.3883246
Mean0.05639586635
Median Absolute Deviation (MAD)0.011033
Skewness12.38076684
Sum4659.426478
Variance0.03692218657
MonotonicityNot monotonic
2022-04-19T12:41:20.977734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0035911
 
< 0.1%
0.00727310
 
< 0.1%
0.00381810
 
< 0.1%
0.00810110
 
< 0.1%
0.00801110
 
< 0.1%
0.00936410
 
< 0.1%
0.0064429
 
< 0.1%
0.0112539
 
< 0.1%
0.0069839
 
< 0.1%
0.0057059
 
< 0.1%
Other values (46047)82523
99.9%
ValueCountFrequency (%)
4.1 × 10-51
< 0.1%
4.5 × 10-51
< 0.1%
5.2 × 10-51
< 0.1%
5.4 × 10-51
< 0.1%
0.0001161
< 0.1%
0.0001231
< 0.1%
0.0001311
< 0.1%
0.0001361
< 0.1%
0.000141
< 0.1%
0.0001471
< 0.1%
ValueCountFrequency (%)
7.34341
< 0.1%
6.17071
< 0.1%
5.85131
< 0.1%
5.55421
< 0.1%
5.44061
< 0.1%
5.42961
< 0.1%
5.19481
< 0.1%
5.17231
< 0.1%
5.03981
< 0.1%
4.90061
< 0.1%

signal_60
Real number (ℝ≥0)

HIGH CORRELATION

Distinct59637
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8687536602
Minimum0.001921
Maximum32.103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2022-04-19T12:41:21.071832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.001921
5-th percentile0.07475985
Q10.2387275
median0.495025
Q30.96809
95-th percentile2.76052
Maximum32.103
Range32.101079
Interquartile range (IQR)0.7293625

Descriptive statistics

Standard deviation1.406888678
Coefficient of variation (CV)1.619433382
Kurtosis97.10888775
Mean0.8687536602
Median Absolute Deviation (MAD)0.309605
Skewness7.536650681
Sum71776.42741
Variance1.979335753
MonotonicityNot monotonic
2022-04-19T12:41:21.153774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0159
 
< 0.1%
1.02279
 
< 0.1%
1.14218
 
< 0.1%
1.02318
 
< 0.1%
1.01728
 
< 0.1%
1.29738
 
< 0.1%
1.01228
 
< 0.1%
1.17368
 
< 0.1%
1.05528
 
< 0.1%
0.435097
 
< 0.1%
Other values (59627)82539
99.9%
ValueCountFrequency (%)
0.0019211
< 0.1%
0.0031371
< 0.1%
0.0031641
< 0.1%
0.0032991
< 0.1%
0.0034551
< 0.1%
0.0036711
< 0.1%
0.0037181
< 0.1%
0.0038721
< 0.1%
0.0040791
< 0.1%
0.0041951
< 0.1%
ValueCountFrequency (%)
32.1031
< 0.1%
31.9141
< 0.1%
31.6011
< 0.1%
31.221
< 0.1%
31.161
< 0.1%
30.9151
< 0.1%
30.2271
< 0.1%
30.1861
< 0.1%
30.0451
< 0.1%
29.7671
< 0.1%

class
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
abnormal
59535 
normal
23085 

Length

Max length8
Median length8
Mean length7.441176471
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
abnormal59535
72.1%
normal23085
 
27.9%

Length

2022-04-19T12:41:21.235792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-19T12:41:21.284803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
abnormal59535
72.1%
normal23085
 
27.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-19T12:41:18.209793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-19T12:41:12.637917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:13.602149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:14.510114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:15.416914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:16.341835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:17.347085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:18.304816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:10.982389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:11.862596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:12.730936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:13.703901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-19T12:41:15.521938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:16.599894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:17.444108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:18.401847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:11.084411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:11.960783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:12.822883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:13.816436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:14.717161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:15.640966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:16.696928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:17.543632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:18.490868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:11.194436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-19T12:41:12.906902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:13.906458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:14.807182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:15.747190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:16.783947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:17.633661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:18.586890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:11.291459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:12.160841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:12.996923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-19T12:41:11.383482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-19T12:41:13.230976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-19T12:41:14.989231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:15.946235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:16.972000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:17.826705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:18.771931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:11.481504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-19T12:41:18.017748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:18.958495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:11.671552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:12.542062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:13.513129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:14.401089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:15.318892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:16.244813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:17.251063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-19T12:41:18.115772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-19T12:41:21.325813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-19T12:41:21.589872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-19T12:41:21.684407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-19T12:41:21.780429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-19T12:41:19.078522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-19T12:41:19.243560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idTimeStampsignal_1signal_10signal_20signal_30signal_40signal_50signal_60class
0000.0009490.0007080.0018330.0019910.0029560.0039710.045608normal
1010.0014880.0002680.0013620.0013540.0032130.0066580.119800normal
2020.0003140.0009710.0018780.0007880.0068840.0286760.178130normal
3030.0009950.0006520.0010340.0010690.0082110.0420230.150210normal
4040.0020990.0007150.0002870.0018510.0043010.0333620.066619normal
5050.0017320.0007100.0014950.0034410.0025050.0114530.058796normal
6060.0020430.0010820.0016380.0040160.0025980.0074950.119240normal
7070.0009550.0017370.0013580.0027220.0028790.0125070.189890normal
8080.0003790.0021040.0016460.0017930.0037120.0135910.218120normal
9090.0010240.0014300.0020180.0014730.0027770.0126740.161990normal

Last rows

idTimeStampsignal_1signal_10signal_20signal_30signal_40signal_50signal_60class
826102033950.0017290.0172510.0054410.0055930.0117440.0194690.405610abnormal
826112033960.0023310.0129110.0035140.0042910.0135610.0128480.362570abnormal
826122033970.0024030.0113830.0026230.0019660.0106450.0165640.264460abnormal
826132033980.0022870.0136460.0026530.0011100.0080320.0252000.139670abnormal
826142033990.0041840.0160750.0028920.0008820.0052240.0301710.099499abnormal
826152034000.0050720.0172480.0005760.0014860.0044700.0303050.131550abnormal
826162034010.0058310.0129850.0028320.0031920.0037830.0182300.177930abnormal
826172034020.0074550.0055760.0039740.0035950.0016190.0078740.158690abnormal
826182034030.0081240.0066730.0020460.0021310.0030420.0066070.068071abnormal
826192034040.0071200.0098710.0007990.0031760.0027880.0106030.130330abnormal